title: A method to screen obstructive sleep apnea using multi-variable non-intrusive measurements.
abstract: Obstructive sleep apnea (OSA) is a serious sleep disorder. The current standard OSA diagnosis method is polysomnography (PSG) testing. PSG requires an overnight hospital stay while physically connected to 10-15 channels of measurement. PSG is expensive, inconvenient and requires the extensive involvement of a sleep technologist. As such, it is not suitable for community screening. OSA is a widespread disease and more than 80% of sufferers remain undiagnosed. Simplified, unattended and cheap OSA screening methods are urgently needed. Snoring is commonly associated with OSA but is not fully utilized in clinical diagnosis. Snoring contains pseudo-periodic packets of energy that produce characteristic vibrating sounds familiar to humans. In this paper, we propose a multi-feature vector that represents pitch information, formant information, a measure of periodic structure existence in snore episodes and the neck circumference of the subject to characterize OSA condition. Snore features were estimated from snore signals recorded in a sleep laboratory. The multi-feature vector was applied to a neural network for OSA/non-OSA classification and K-fold cross-validated using a random sub-sampling technique. We also propose a simple method to remove a specific class of background interference. Our method resulted in a sensitivity of 91 ± 6% and a specificity of 89 ± 5% for test data for AHI(THRESHOLD) = 15 for a database consisting of 51 subjects. This method has the potential as a non-intrusive, unattended technique to screen OSA using snore sound as the primary signal.
authors.affiliation: School of Information Technology and Electrical Engineering, University of Queensland, St Lucia, Brisbane, Australia.
authors.forename: S
authors.initials: S
authors.lastname: de Silva
authors.forename: U R
authors.initials: UR
authors.lastname: Abeyratne
authors.forename: C
authors.initials: C
authors.lastname: Hukins
mesh_terms: D019411:Clinical Laboratory Techniques / Q000379:methods*; D016208:Databases, Factual; D006801:Humans; D008297:Male; D008875:Middle Aged; D009333:Neck / Q000033:anatomy & histology; D016571:Neural Networks, Computer; D011336:Probability; D015203:Reproducibility of Results; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis*; D012913:Snoring; D013016:Sound; D013997:Time Factors